Difference between revisions of "Fall 2022 Schedule"

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| 1.22.20 || Abe Handler (PhD student from UMass)
 
| 1.22.20 || Abe Handler (PhD student from UMass)
 
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| 1.29.20 || Lingfeng Jin's talk at 4:00pm loc: Muen D430
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| 1.29.20 || Lifeng Jin's talk at 4:00pm loc: Muen D430, Modeling Syntax Acquisition with Cognitive Constraints
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Abstract: Children seem to learn their first languages effortlessly, but how they are able to do this has been heatedly debated for many years among linguists.
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Recent advances in computational linguistics have presented us a unique opportunity to explore the problem of syntax acquisition with computational modeling.  In this talk, I will first introduce syntax acquisition modeling, also known as grammar induction, from a theoretical perspective. I will then present our efforts in modeling syntax acquisition with statistical machine learning models with human memory constraints. Simulations using Bayesian and neural network models on natural data in many languages have provided insight into how language acquisition may happen without universals as inductive biases as well as how cognitive constraints may interact with syntax acquisition. Finally I will discuss some theoretical considerations and future directions for acquisition modeling.
 
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| 2.5.20 || Katharina practice talk (Meta-learning for morphological inflection)
 
| 2.5.20 || Katharina practice talk (Meta-learning for morphological inflection)

Revision as of 12:23, 27 January 2020

Date Title
1.15.20 Planning
1.22.20 Abe Handler (PhD student from UMass)
1.29.20 Lifeng Jin's talk at 4:00pm loc: Muen D430, Modeling Syntax Acquisition with Cognitive Constraints

Abstract: Children seem to learn their first languages effortlessly, but how they are able to do this has been heatedly debated for many years among linguists. Recent advances in computational linguistics have presented us a unique opportunity to explore the problem of syntax acquisition with computational modeling. In this talk, I will first introduce syntax acquisition modeling, also known as grammar induction, from a theoretical perspective. I will then present our efforts in modeling syntax acquisition with statistical machine learning models with human memory constraints. Simulations using Bayesian and neural network models on natural data in many languages have provided insight into how language acquisition may happen without universals as inductive biases as well as how cognitive constraints may interact with syntax acquisition. Finally I will discuss some theoretical considerations and future directions for acquisition modeling.

2.5.20 Katharina practice talk (Meta-learning for morphological inflection)
2.12.20 Daniel prelim presentation (prepositional polysemy)
2.19.20 Sebastian Schuster 17th 4:00pm & Adina Williams 19th 4:00pm & Chris Potts 21st 12:00PM loc: MUEN D430
2.26.20 Alexis Palmer 4:00pm
3.4.20
3.11.20 Vivian prelim
3.18.20 CLASIC capstone
3.25.20 Spring Break - No Meeting
4.1.20 COLING paper clinic
4.8.20 Rehan Prelim
4.15.20 Skatje's proposal
4.22.20 Chelsea proposal
4.29.20 Jon prelim
5.2.18 EMNLP paper clinic
5.6.20 Finals week

Past Schedules